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Date:      Sun, 14 Jun 2020 14:22:35 +0000 (UTC)
From:      Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org>
To:        ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org
Subject:   svn commit: r538750 - head/science/liblinear
Message-ID:  <202006141422.05EEMZsI093776@repo.freebsd.org>

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Author: sunpoet
Date: Sun Jun 14 14:22:35 2020
New Revision: 538750
URL: https://svnweb.freebsd.org/changeset/ports/538750

Log:
  Update to 2.30
  
  - Update MASTER_SITES
  - Add my LOCAL to MASTER_SITES
  - Remove DOCS and OPTIMIZED_CFLAGS options
  - Update pkg-descr
  - Update WWW
  - Take maintainership
  
  Changes:	https://github.com/cjlin1/liblinear/commits/master

Modified:
  head/science/liblinear/Makefile
  head/science/liblinear/distinfo
  head/science/liblinear/pkg-descr

Modified: head/science/liblinear/Makefile
==============================================================================
--- head/science/liblinear/Makefile	Sun Jun 14 14:22:30 2020	(r538749)
+++ head/science/liblinear/Makefile	Sun Jun 14 14:22:35 2020	(r538750)
@@ -2,37 +2,20 @@
 # $FreeBSD$
 
 PORTNAME=	liblinear
-PORTVERSION=	2.11
+PORTVERSION=	2.30
 CATEGORIES=	science math
-MASTER_SITES=	http://www.csie.ntu.edu.tw/~cjlin/liblinear/ \
-		http://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/
+MASTER_SITES=	https://www.csie.ntu.edu.tw/~cjlin/liblinear/ \
+		https://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/
 
-MAINTAINER=	ports@FreeBSD.org
+MAINTAINER=	sunpoet@FreeBSD.org
 COMMENT=	Library for Large Linear Classification
 
 LICENSE=	BSD3CLAUSE
 LICENSE_FILE=	${WRKSRC}/COPYRIGHT
 
-USES=		zip
+PLIST_FILES=	bin/predict bin/train
 
-PORTDOCS=	COPYRIGHT README
-
-PLIST_FILES=	bin/train bin/predict
-
-OPTIONS_DEFINE=	OPTIMIZED_CFLAGS DOCS
-OPTIONS_DEFAULT=OPTIMIZED_CFLAGS
-
-# same as LIBLINEAR itself
-OPTIMIZED_CFLAGS_CFLAGS=		-Wall -O3
-
 do-install:
-	${INSTALL_PROGRAM} ${WRKSRC}/train ${STAGEDIR}${PREFIX}/bin
-	${INSTALL_PROGRAM} ${WRKSRC}/predict ${STAGEDIR}${PREFIX}/bin
-
-do-install-DOCS-on:
-	@${MKDIR} ${STAGEDIR}${DOCSDIR}
-	for f in ${PORTDOCS}; do \
-		${INSTALL_DATA} ${WRKSRC}/$$f ${STAGEDIR}${DOCSDIR}; \
-	done
+	${INSTALL_PROGRAM} ${WRKSRC}/predict ${WRKSRC}/train ${STAGEDIR}${PREFIX}/bin
 
 .include <bsd.port.mk>

Modified: head/science/liblinear/distinfo
==============================================================================
--- head/science/liblinear/distinfo	Sun Jun 14 14:22:30 2020	(r538749)
+++ head/science/liblinear/distinfo	Sun Jun 14 14:22:35 2020	(r538750)
@@ -1,3 +1,3 @@
-TIMESTAMP = 1497777405
-SHA256 (liblinear-2.11.zip) = f1f263f4b22530f07d298a1c4812d675ed879e4f7d3801abc9a637f62a505ce0
-SIZE (liblinear-2.11.zip) = 522832
+TIMESTAMP = 1591281350
+SHA256 (liblinear-2.30.tar.gz) = 881c7039c6cf93119c781fb56263de91617b3eca8c3951f2c19a3797de95c6ac
+SIZE (liblinear-2.30.tar.gz) = 526468

Modified: head/science/liblinear/pkg-descr
==============================================================================
--- head/science/liblinear/pkg-descr	Sun Jun 14 14:22:30 2020	(r538749)
+++ head/science/liblinear/pkg-descr	Sun Jun 14 14:22:35 2020	(r538750)
@@ -1,14 +1,20 @@
 LIBLINEAR is a linear classifier for data with millions of instances and
-features. It supports L2-regularized classifiers (L2-loss linear SVM,
-L1-loss linear SVM, and logistic regression), L1-regularized classifiers
-(L2-loss linear SVM and logistic regression).
+features. It supports:
+- L2-regularized classifiers
+- L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
+- L1-regularized classifiers (after version 1.4)
+- L2-loss linear SVM and logistic regression (LR)
+- L2-regularized support vector regression (after version 1.9)
+- L2-loss linear SVR and L1-loss linear SVR.
 
-Main features of LIBLINEAR include
-
-- Same data format as LIBSVM and similar usage
-- One-vs-the rest and Crammer & Singer multi-class classification
-- Cross validation for model selection
+Main features of LIBLINEAR include:
+- Same data format as LIBSVM, our general-purpose SVM solver, and also similar
+  usage
+- Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
+- Cross validation for model evaulation
+- Automatic parameter selection
 - Probability estimates (logistic regression only)
 - Weights for unbalanced data
+- MATLAB/Octave, Java, Python, Ruby interfaces
 
-WWW: http://www.csie.ntu.edu.tw/~cjlin/liblinear/
+WWW: https://www.csie.ntu.edu.tw/~cjlin/liblinear/



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